Protein−Ligand Binding Free Energy Calculation by the Smooth Reaction Path Generation (SRPG) Method

Computer simulation of molecular recognition in biomolecular system: from in silico screening to generalized ensembles

Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.

Assessing the Performance of Screening MM/PBSA in Protein-Ligand Interactions

Accurate calculation of the binding free energies between a protein and a ligand is the primary objective of structure-based drug design, but it still remains a challenging problem. In this work, we apply the screening molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) method to calculate the binding affinity of protein-ligand interactions. Our results show that the performance of the screening MM/PBSA is better than that of the standard MM/PBSA, especially in a charged-ligand system. In addition, we also investigate the effect of the solute dielectric constant on the results, and find that the optimal solute dielectric constants are different between the neutral-ligand system and the charged-ligand system. Moreover, we also evaluate the effect of the atomic-charge methods on the performance of the screening MM/PBSA. The present study demonstrates that the screening MM/PBSA should be a reliable method for calculating binding energy of biosystems.

Molecular Dynamics Simulation Study on Allosteric Regulation of CD44-Hyaluronan Binding as a Force Sensing Mechanism

CD44 protein exists on surfaces of a variety of human cells, acts as a receptor for the hyaluronan (HA) molecule, and mediates cell adhesion via the HA binding in leukocyte trafficking, cell rolling, and so on. The molecular structures of both CD44 and HA are well known, and the previous work shows that the external-mechanical force induces the partially disordered (PD) conformation from the ordered (O) conformation of CD44. The PD conformation has the higher HA affinity compared to the O conformation. However, the details of force-sensing mechanics have remained unclear. This study provides new insights into allosteric regulation of HA binding by conformational shift from the O to the PD conformation of the CD44 HA binding domain by using the classical molecular dynamics simulations. The O conformation was more favorable than the PD conformation under the equilibrium state, and the O conformation showed weak HA-binding affinity. Our simulation suggests that the PD conformation induced by the external force can refold to a compact structure similar to the O conformation keeping the bound HA. This new conformation showed a higher affinity than the O and PD conformations. Our results show that the unfolding of a remote disordered region from the ligand binding site by the external force allosterically regulates the HA affinity. This study promotes understanding not only the mechanism of CD44-mediated cell rolling but also the allosteric regulation induced by the external mechanical force.

Accurate prediction of relative binding affinities of a series of HIV-1 protease inhibitors using semi-empirical quantum mechanical charge

A major challenge in computer-aided drug design is the accurate estimation of ligand binding affinity. Here, a new approach that combines the adaptive steered molecular dynamics (ASMD) and partial atomic charges calculated by semi-empirical quantum mechanics (SQMPC), namely ASMD-SQMPC, is suggested to predict the ligand binding affinities, with 24 HIV-1 protease inhibitors as testing examples. In the ASMD-SQMPC, the relative binding free energy (ΔG) is reflected by the average maximum potential of mean force (<PMF>_max ) between bound and unbound states. The correlation coefficient (R² ) between the <PMF>_max and experimentally determined ΔG is 0.86, showing a significant improvement compared with the conventional ASMD (R² = 0.52). Therefore, this study provides an efficient approach to predict the relative ΔG and reveals the significance of precise partial atomic charges in the theoretical simulations.

Vitamin K1 prevents diabetic cataract by inhibiting lens aldose reductase 2 (ALR2) activity

This study investigated the potential of vitamin K1 as a novel lens aldose reductase inhibitor in a streptozotocin-induced diabetic cataract model. A single, intraperitoneal injection of streptozotocin (STZ) (35 mg/kg) resulted in hyperglycemia, activation of lens aldose reductase 2 (ALR2) and accumulation of sorbitol in eye lens which could have contributed to diabetic cataract formation. However, when diabetic rats were treated with vitamin K1 (5 mg/kg, sc, twice a week) it resulted in lowering of blood glucose and inhibition of lens aldose reductase activity because of which there was a corresponding decrease in lens sorbitol accumulation. These results suggest that vitamin K1 is a potent inhibitor of lens aldose reductase enzyme and we made an attempt to understand the nature of this inhibition using crude lens homogenate as well as recombinant human aldose reductase enzyme. Our results from protein docking and spectrofluorimetric analyses clearly show that vitamin K1 is a potent inhibitor of ALR2 and this inhibition is primarily mediated by the blockage of DL-glyceraldehyde binding to ALR2. At the same time docking also suggests that vitamin K1 overlaps at the NADPH binding site of ALR2, which probably shows that vitamin K1 could possibly bind both these sites in the enzyme. Another deduction that we can derive from the experiments performed with pure protein is that ALR2 has three levels of affinity, first for NADPH, second for vitamin K1 and third for the substrate DL-glyceraldehyde. This was evident based on the dose-dependency experiments performed with both NADPH and DL-glyceraldehyde. Overall, our study shows the potential of vitamin K1 as an ALR2 inhibitor which primarily blocks enzyme activity by inhibiting substrate interaction of the enzyme. Further structural studies are needed to fully comprehend the exact nature of binding and inhibition of ALR2 by vitamin K1 that could open up possibilities of its therapeutic application.

The Effect of Conformational Flexibility on Binding Free Energy Estimation between Kinases and Their Inhibitors

Accurate prediction of binding affinities of drug candidates to their targets remains challenging because of protein flexibility in solution. Conformational flexibility of the ATP-binding site in the CDK2 and ERK2 kinases was identified using molecular dynamics simulations. The binding free energy (ΔG) of twenty-four ATP-competitive inhibitors toward these kinases was assessed using an alchemical free energy perturbation method, MP-CAFEE. However, large calculation errors of 2-3 kcal/mol were observed using this method, where the free energy simulation starts from a single equilibrated conformation. Here, we developed a new ΔG computation method, where the starting structure was set to multiconformations to cover flexibility. The calculation accuracy was successfully improved, especially for larger molecular size compounds, leading to reliable prediction of a broader range of drug candidates. The present study demonstrates that conformational flexibility of interactions between a compound and the glycine-rich loop in the kinases is a key factor in ΔG estimation.

Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods

Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

Improved estimation of protein-ligand binding free energy by using the ligand-entropy and mobility of water molecules

We previously developed the direct interaction approximation (DIA) method to estimate the protein-ligand binding free energy (DG). The DIA method estimates the DG value based on the direct van der Waals and electrostatic interaction energies between the protein and the ligand. In the current study, the effect of the entropy of the ligand was introduced with protein dynamic properties by molecular dynamics simulations, and the interaction between each residue of the protein and the ligand was also weighted considering the hydration of each residue. The molecular dynamics simulation of the apo target protein gave the hydration effect of each residue, under the assumption that the residues, which strongly bind the water molecules, are important in the protein-ligand binding. These two effects improved the reliability of the DIA method. In fact, the parameters used in the DIA became independent of the target protein. The averaged error of DG estimation was 1.3 kcal/mol and the correlation coefficient between the experimental DG value and the calculated DG value was 0.75.

Current progress in Structure-Based Rational Drug Design marks a new mindset in drug discovery

The past decade has witnessed a paradigm shift in preclinical drug discovery with structure-based drug design (SBDD) making a comeback while high-throughput screening (HTS) methods have continued to generate disappointing results. There is a deficit of information between identified hits and the many criteria that must be fulfilled in parallel to convert them into preclinical candidates that have a real chance to become a drug. This gap can be bridged by investigating the interactions between the ligands and their receptors. Accurate calculations of the free energy of binding are still elusive; however progresses were made with respect to how one may deal with the versatile role of water. A corpus of knowledge combining X-ray structures, bioinformatics and molecular modeling techniques now allows drug designers to routinely produce receptor homology models of increasing quality. These models serve as a basis to establish and validate efficient rationales used to tailor and/or screen virtual libraries with enhanced chances of obtaining hits. Many case reports of successful SBDD show how synergy can be gained from the combined use of several techniques. The role of SBDD with respect to two different classes of widely investigated pharmaceutical targets: (a) protein kinases (PK) and (b) G-protein coupled receptors (GPCR) is discussed. Throughout these examples prototypical situations covering the current possibilities and limitations of SBDD are presented.

Protein-Ligand Binding from Distancefield Distances and Hamiltonian Replica Exchange Simulations

The calculation of protein-ligand binding free energies is an important goal in the field of computational chemistry. Applying path-sampling methods for this purpose involves calculating the associated potential of mean force (PMF) and gives insight into the binding free energy along the binding process. Without a priori knowledge about the binding path, sampling reversible binding can be difficult to achieve. To alleviate this problem, we introduce the distancefield (DF) as a reaction coordinate for such calculations. DF is a grid-based method in which the shortest distance between the binding site and a ligand is determined avoiding routes that pass through the protein. Combining this reaction coordinate with Hamiltonian replica exchange molecular dynamics (HREMD) allows for the reversible binding of the ligand to the protein. A comparison is made between umbrella sampling using regular distance restraints and HREMD with DF restraints to study aspirin binding to the protein phospholipase A2. Although the free energies of binding are similar for both methods, the increased sampling with HREMD has a significant influence on the shape of the PMF. A remarkable agreement between the calculated binding free energies from the PMF and the experimental estimate is obtained.

Statistical estimation of the protein-ligand binding free energy based on direct protein-ligand interaction obtained by molecular dynamics simulation

We have developed a method for estimating protein-ligand binding free energy (DG) based on the direct protein-ligand interaction obtained by a molecular dynamics simulation. Using this method, we estimated the DG value statistically by the average values of the van der Waals and electrostatic interactions between each amino acid of the target protein and the ligand molecule. In addition, we introduced fluctuations in the accessible surface area (ASA) and dihedral angles of the protein-ligand complex system as the entropy terms of the DG estimation. The present method included the fluctuation term of structural change of the protein and the effective dielectric constant. We applied this method to 34 protein-ligand complex structures. As a result, the correlation coefficient between the experimental and calculated DG values was 0.81, and the average error of DG was 1.2 kcal/mol with the use of the fixed parameters. These results were obtained from a 2 nsec molecular dynamics simulation.

Free energy calculations of protein-ligand interactions

In the calculation of free energies of binding for protein-ligand complexes, we distinguish endpoint methods, methods involving alchemical modifications and methods that physically displace the ligand from the protein. Most methodological advances seem to come from a clever combination of multiple existing methods to enhance the sampling or to utilize specific advantages of various approaches. The coupling parameters common in thermodynamic integration and in Hamiltonian replica exchange are for instance combined to yield replica exchange thermodynamic integration. As new methods mostly aim to improve efficiency or to attain more complete sampling, there are good prospects to understand and tackle the sampling problem better and to shift the focus towards the scoring problem in the context of more robust and accurate force fields.

Structural ensemble in computational drug screening

IMPORTANCE OF THE FIELD

Structure-based in silico drug screening is now widely used in drug development projects. Structure-based in silico drug screening is generally performed using a protein-compound docking program and docking scoring function. Many docking programs have been developed over the last 2 decades, but their prediction accuracy remains insufficient.

AREAS COVERED IN THIS REVIEW

This review highlights the recent progress of the post-processing of protein-compound complexes after docking.

WHAT THE READER WILL GAIN

These methods utilize ensembles of docking poses of compounds to improve the prediction accuracy for the ligand-docking pose and screening results. While the individual docking poses are not reliable, the free energy surface or the most probable docking pose can be estimated from the ensemble of docking poses.

TAKE HOME MESSAGE

The protein-compound docking program provides an arbitral rather than a canonical ensemble of docking poses. When the ensemble of docking poses satisfies the canonical ensemble, we can discuss how these post-docking analysis methods work and fail. Thus, improvements to the docking software will be needed in order to generate well-defined ensembles of docking poses.

Development of CHARMM polarizable force field for nucleic acid bases based on the classical Drude oscillator model

A polarizable force field for nucleic acid bases based on the classical Drude oscillator model is presented. Parameter optimization was performed to reproduce crystallographic geometries, crystal unit cell parameters, heats of sublimation, vibrational frequencies and assignments, dipole moments, molecular polarizabilities and quantum mechanical base-base and base-water interaction energies. The training and validation data included crystals of unsubstituted and alkyl-substituted adenine, guanine, cytosine, uracil, and thymine bases, hydrated crystals, and hydrogen bonded base pairs. Across all compounds, the RMSD in the calculated heats of sublimation is 4.1%. This equates to an improvement of more than 2.5 kcal/mol in accuracy compared to the nonpolarizable CHARMM27 force field. However, the level of agreement with experimental molecular volume decreased from 1.7% to 2.1% upon moving from the nonpolarizable to the polarizable model. The representation of dipole moments is significantly improved with the Drude polarizable force field. Unlike in additive force fields, there is no requirement for the gas-phase dipole moments to be overestimated, illustrating the ability of the Drude polarizable force field to treat accurately differently dielectric environments and indicating the improvements in the electrostatic model. Validation of the model was performed on the basis of the calculation of the gas-phase binding enthalpies of base pairs obtained via potential of mean force calculations; the additive and polarizable models both performed satisfactorily with average differences of 0.2 and 0.9 kcal/mol, respectively, and rms differences of 1.3 and 1.7 kcal/mol, respectively. Overall, considering the number of significant improvements versus the additive CHARMM force field, the incorporation of explicit polarizability into the force field for nucleic acid bases represents an additional step toward accurate computational modeling of biological systems.

A Note on the Standard State's Binding Free Energy

The relation between the equilibrium constant of a given chemical reaction and the associated free energy is an issue well studied in chemistry books, but when the reaction involves changes in the number of components in the system, as is the case in binding, things become a little more obscure since one needs to define the so-called standard state. This is reflected in the literature, especially in computational studies of binding, where contradicting approaches are followed when treating this problem. In this work, we present a detailed and unifying explanation of the concepts involved and derive the necessary relations to convert a binding free energy from an arbitrary state to some given standard state. This is done in three independent ways, from the point of view of (1) the dimensions of the quantities involved, (2) the energy and entropy of the molecules, and (3) their chemical potentials.

Therapeutic strategies underpinning the development of novel techniques for the treatment of HIV infection

The HIV replication cycle offers multiple targets for chemotherapeutic intervention, including the viral exterior envelope glycoprotein, gp120; viral co-receptors CXCR4 and CCR5; transmembrane glycoprotein, gp41; integrase; reverse transcriptase; protease and so on. Most currently used anti-HIV drugs are reverse transcriptase inhibitors or protease inhibitors. The expanding application of simulation to drug design combined with experimental techniques have developed a large amount of novel inhibitors that interact specifically with targets besides transcriptase and protease. This review presents details of the anti-HIV inhibitors discovered with computer-aided approaches and provides an overview of the recent five-year achievements in the treatment of HIV infection and the application of computational methods to current drug design.